A Multistage Screening Model for Evaluation and Control of Misclassification Error in the Detection of Hypertension

Hypertension is one of the most important risk factors with respect to coronary heart disease and stroke. The benefits of early detection of hypertension and the subsequent design of follow-up treatment programs are well documented. Consequently, screening programs have been designed to identify subjects as normotensive (normal) or hypertensive (abnormal). In order for these programs to be effective, full participation of the subject population is required. However, such classification programs can incur massive risks of incorrectly classifying subjects as normotensive who are truly hypertensive and incorrectly classifying subjects as hypertensive who are truly normotensive. To date, the only means to reduce these risks of misclassification is to require subjects to make numerous visits for blood pressure measurement before they can be classified. Such requirements reduce the level of participation in screening programs and also delay the identification of subjects who are truly hypertensive, thereby depriving them of the benefits of early detection and immediate follow-up treatment. We propose a multiple-stage screening model that controls for maximum as well as average misclassification error which is used to design and/or evaluate screening programs for hypertension. A multiple-stage screening model not only permits the early detection of subjects who are truly hypertensive, but also requires a much smaller level of participation of subjects, while retaining control of misclassification risks that are comparable to those of screening programs based on numerous visits. We then design multiple-stage screening programs for several different subject populations.